148 8 Cognitive Synergy processes in terms of the “analysis vs. synthesis” distinction. Finally, Tables ?? and ?? exemplify these structures and processes in the context of embodied virtual agent control. In the CogPrime context, a procedure in this cognitive schematic is a program tree stored in the system’s procedural knowledge base; and a context is a (fuzzy, probabilistic) logical predicate stored in the AtomSpace, that holds, to a certain extent, during each interval of time. A goal is a fuzzy logical predicate that has a certain value at each interval of time, as well. Attentional knowledge is handled in CogPrime by the ECAN artificial economics mechanism, that continually updates ShortTermImportance and LongTerm Importance values associated with each item in the CogPrime system’s memory, which control the amount of attention other cognitive mechanisms pay to the item, and how much motive the system has to keep the item in memory. HebbianLinks are then created between knowledge items that often possess ShortTermImportance at the same time; this is CogPrime’s version of traditional Hebbian learning. ECAN has deep interactions with other cognitive mechanisms as well, which are essential to its efficient operation; for instance, PLN inference may be used to help ECAN extrapolate conclusions about what is worth paying attention to, and MOSES may be used to recognize subtle attentional patterns. ECAN also handles “assignment of credit”, the figuring-out of the causes of an instance of successful goal-achievement, drawing on PLN and MOSES as needed when the causal inference involved here becomes difficult. The synergies between CogPrime’s cognitive processes are well summarized below, which is a 16x16 matrix summarizing a host of interprocess interactions generic to CST. One key aspect of how CogPrime implements cognitive synergy is PLN’s sophisticated man- agement of the confidence of judgments. This ties in with the way OpenCogPrime’s PLN in- ference framewor